This episode explores how the foundations of AI hardware are being rethought in response to the growing energy demands of large language models. As modern AI systems strain power budgets due to memory movement and dense computation on GPUs, researchers are turning to neuromorphic and photonic computing for more sustainable paths forward. The discussion covers spiking neural networks, which process information through sparse, event-driven signals that resemble biological brains and dramatically reduce wasted computation. We examine advances such as IBM’s NorthPole architecture, Intel’s Loihi chips, and memristor-based artificial neurons that combine memory and computation at the device level. The episode also highlights the role of emerging software frameworks that make these architectures programmable and practical. Together, these developments point toward an AI future built on bio-mimetic circuits and optical components, offering a scalable and energy-efficient alternative to today’s power-hungry models.
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